A sectional image will be described taking for example a CT image obtained from an X-ray CT (Computed Tomography) apparatus which picks up images by revolving an imaging system including an X-ray tube (emitting device) and a detector (detecting device) about the body axis of a patient. X-ray CT apparatus are medical equipment indispensable to clinical practice, and include a single slice CT apparatus, a multi-slice CT apparatus with detector cells juxtaposed along the direction of a body axis, and a cone-beam CT apparatus which emits a cone-shaped X-ray beam spreading along the direction of a body axis from an X-ray tube. As a detector provided for an X-ray CT apparatus, a flat panel X-ray detector (hereinafter abbreviated as “FPD”) has recently been used in cone-beam CT.
In the case of an X-ray CT apparatus, with the imaging system revolved about the body axis of a patient, an artifact appears in a ring shape on a CT image (hereinafter abbreviated as “ring artifact”). This will particularly be described with reference to FIG. 14. FIG. 14 is a schematic view for use in description of ring artifact generation on a CT image. In FIG. 14, a single slice CT apparatus will be described by way of example, for the sake of brief description. Generally, a ring artifact is generated on a CT image by deficiency or sensitivity degradation of a cell (see the cell affixed with sign “D” in FIG. 14(a)) of a detector which is represented by an X-ray detecting array 4, for example. By revolving a channel detector in one row and an X-ray tube 2 forming a pair about the body axis z of a patient M, original data (called “sinogram”) is acquired which, as shown in FIG. 14(b), has a horizontal axis representing the direction of arrangement of the cells of the channel detector (also called “Channel direction”), and a vertical axis representing the direction of projection (also called “View direction”). When a certain cell of the detector has deficiency or sensitivity variation D as shown in FIG. 14(a), a linear artifact ART1 will appear on the sinogram and, as shown in FIG. 14(c), a ring-like artifact (ring artifact) ART2 will appear on a reconstructed CT image. Even a sensitivity difference of only 0.1% of the detector can become clearly visible as a ring.
Conventionally, it has been general practice to remove ring artifacts on CT images by interpolating a deficient pixel on a sinogram, or through a sensitivity correction of the detector. On the other hand, there are Patent Documents 1 and 2 as a technique of removing ring artifacts by processing on CT images. In Patent Documents 1 and 2, a distribution of variations in the belt-like width of the ring or luminance (pixel value) is checked beforehand by collecting and observing ring artifacts on the CT images. A lowpass filter (low-pass type filter), a median filter or the like is applied directly to the ring artifacts meeting these conditions, thereby to remove the ring artifacts.
Incidentally, Independent Component Analysis (ICA) has been proposed as a multi-dimension signal analyzing method in recent years, which observes mixed signals having independent signals overlapping one another, and separates them into independent original signals (see Nonpatent Documents 1-5, for example). Nonpatent Document 2, in particular, applies Independent Component Analysis (ICA) to an image, and develops the image with a basis function, to calculate a characteristic vector and analyzes the characteristic of the image.
[Patent Document 1]
Patent National Publication H11-514121
[Patent Document 2]
Unexamined Patent Publication H11-128218 [Nonpatent Document 1]
Chin En-i, “Independent Component Analysis (1)—Cocktail Party Effect—”, Japanese Society of Medical Imaging Technology, 2003, Vol. 21, No. 1, p. 81-85
[Nonpatent Document 2]
Chin En-i, “Independent Component Analysis (2)—Characteristic Extraction by ICA base—”, Japanese Society of Medical Imaging Technology, 2003, Vol. 21, No. 2, p. 170-174
[Nonpatent Document 3]
Noboru Murata, “Introduction—Independent Component Analysis”, Tokyo Denki University Press, July, 2004
[Nonpatent Document 4]
Aapo Hyvarinen, Juha Karhunen & Erkki Oja, translated by Chikashi Nemoto & Masayoshi Kawato “Detailed Description: Independent Component Analysis—New World of Signal Analysis—” Tokyo Denki University Press, February, 2005
[Nonpatent Document 5]
Murata Noboru, “An Introduction to Independent Component Analysis”, May 1, Heisei 14, Waseda University, Department of Science and Engineering, Faculty of Electric, electronics and information engineering, [online] Internet <URL: http://www.eb.waseda.ac.jp/murata/˜mura/lecture/ica/note/>